Efficient Management Research Articles

A Blockchain and Hybrid Deep Learning for Secure and Efficient Healthcare Data Transmission and Management

A Blockchain and Hybrid Deep Learning for Secure and Efficient Healthcare Data Transmission and Management

The innovation of engineering project management in the context of big data

ABSTRACT With the development of China’s economy and society, the society continues to move forward. Related to this, the scale of China’s engineering construction is also increasing. With the development of the times and the continuous growth of demand, the complexity and specialisation of engineering projects continue to improve. This has put forward more stringent requirements for the construction of engineering projects. As the traditional engineering project construction management in China, the innovation is mainly through the practice of construction projects and the research of the engineering problems that arise. So, in today’s environment of big data background, the innovation of construction project management is to combine with the actual situation of construction projects, from the construction characteristics of the project, using a variety of advanced scientific technology, for the project data collection, calculation and storage applications and other aspects of optimisation and innovation, so that more convenient to achieve The modern construction engineering project management innovation work of diversified needs, for the final completion of the entire construction project can be expected to provide a strong guarantee and support the comprehensive benefits of the goal. The results show that our innovative scheme outperforms other comparative methods and achieves a 23% improvement in project management efficiency.

A joint work package sizing and scheduling problem considering resource constraints with a look-ahead heterogeneous reinforcement learning method

Effective work package sizing and project scheduling are crucial for construction project management. However, existing studies often address them as separate optimisation problems, neglecting the interactive nature of these processes. In practical scenarios with limited resources, there is an increasing demand for integrating work package sizing and project scheduling to enhance project management efficiency. This research aims to bridge this gap by developing an integer non-linear programming model that incorporates work package sizing and project scheduling while considering their interaction within a resource-constrained environment. Moreover, we introduce a novel look-ahead heterogeneous reinforcement learning approach that dynamically adjusts work package sizes and project schedules based on observed information at each decision step. A look-ahead sequential decision mechanism is proposed to effectively address the interdependencies and constraints inherent in the joint model. We extend the heterogeneous agent mirror learning technique to our problem to improve sample efficiency and ensure progressive enhancement of the joint policy. To evaluate our approach's effectiveness, we conduct experiments using datasets of 15, 30, 60, 90, and 120 tasks from Rangen and PSPLIB and validate our method in a real-world modular integrated construction project. The experimental results reveal that a joint model integrating work package sizing and project scheduling offers a more comprehensive understanding of their interplay, leading to better resource utilisation and improved project schedules. A comparative analysis against existing state-of-the-art reinforcement learning and priority rule-based methods further substantiates the superior effectiveness of our proposed approach, yielding an approximate 5% reduction in total project cost.

Energy management in smart distribution networks: Synergizing network reconfiguration, energy storage, and electric vehicles with disjunctive convex hull relaxation

Efficient energy management is critical for modern distribution networks integrating renewable energy, storage systems, and electric vehicles. This paper introduces a novel approach combining network reconfiguration with disjunctive convex hull relaxation to optimize power flow, minimize energy losses, and enhance grid stability. Case studies on 33-node and 84-node networks showcase the model’s effectiveness, reducing energy losses to 0.939 MWh and 7.214 MWh, respectively, outperforming Big-M and McCormick methods. Additionally, the approach achieved a 78 % efficiency improvement, enhanced emissions control, and improved voltage regulation, demonstrating superior renewable energy integration. These results highlight the model's potential to provide a more efficient and sustainable energy management solution, addressing the challenges of modern power grids.

The Design of a Good Data Processing App Applying QR Code

Designing a good data processing application using QR Codes aims to increase efficiency and accuracy in data management. QR codes are a fast and easy technology for storing and scanning data, allowing users to quickly access information without entering data manually. This application is designed for various purposes, such as inventory, time and attendance management, and document tracking. The application design process includes user needs analysis, interface design, back-end development, and integration with QR code technology. The prototype method is used during the system development stage. The programming language used is a hypertext preprocessor with a MySQL database, and a framework is used to ensure that the application behaves as expected and can provide effective solutions in data processing. The results of this design are expected to significantly contribute to operational efficiency, reduce human error in data entry, and increase the speed of accessing and managing information. Apart from that, implementing QR codes in data processing applications is hoped to be widely used in various industries and business fields.

PREDICTPredicting and Reducing Patient Waiting Times in Dental Clinics Using Machine Learning: A Case Study from TürkiyeING AND REDUCING PATIENT WAITING TIMES IN DENTAL CLINICS USING MACHINE LEARNING: A CASE STUDY FROM TURKEY

Long waiting times in polyclinics are a critical factor affecting patient satisfaction and the efficient use of healthcare personnel and resources. This study applied machine learning (ML) algorithms to predict and reduce patient waiting times in a dental clinic in Turkey. The daily data collected from the clinic included variables such as patient satisfaction, appointment patients, Walk-in patients, number of doctors and nurses, and dental technicians on duty. Six ML algorithms were tested: Decision Trees (DT), Linear Regression (LR), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Kernel Regression (KR), and Neural Networks (NN). Among these, the GPR model achieved the best performance, accurately predicting patient waiting times with an R2 value of 0.936 and RMSE of 0.075. This study highlights the potential of ML methods to enhance operational efficiency in healthcare management.

Quantifying and Mitigating Greenhouse Gas Emissions in Egg-Laying Hen Farming: A Path Towards Carbon Neutrality

This study presents a comprehensive framework for assessing and mitigating greenhouse gas (GHG) emissions in commercial egg-laying hen farming in Uttaradit Province, with the aim of achieving carbon neutrality within the poultry sector. Employing a detailed life cycle assessment (LCA) over a 450-day production period, this research identifies key sources of emissions, with feed consumption identified as the largest contributor, followed by water use and energy demands. To address these emissions, the study explores several innovative strategies: transitioning to solar photovoltaic systems for lighting and water pumping, shifting from diesel to biodiesel for fuel, and optimizing feed compositions. Additionally, advanced manure management practices are proposed to reduce methane and nitrous oxide emissions. Collectively, these interventions could significantly diminish the emissions associated with hen farming operations, thereby advancing environmental sustainability. This work not only provides actionable insights for poultry farms seeking to lower their emissions but also offers a scalable and adaptable model with broader implications for sustainable practices across the agricultural sector. The findings underscore the importance of renewable energy integration, feed optimization, and efficient waste management in mitigating the environmental impact of agriculture, thereby informing both policy and practice in the pursuit of carbon-neutral food production.

Implementation and evaluation of digital twin framework for Internet of Things based healthcare systems

AbstractThe integration of digital twins (DTs) in healthcare is critical but remains limited in real‐time patient monitoring due to challenges in achieving low‐latency telemetry transmission and efficient resource management. This paper addresses these limitations by presenting a novel cloud‐based DT framework that optimises real‐time healthcare monitoring, providing a timely solution for critical healthcare needs. The framework incorporates a Pyomo‐based dynamic optimisation model, which reduces telemetry latency by 32% and improves response time by 52%, surpassing existing systems. Leveraging low‐cost, low‐latency multimodal sensors, the system continuously monitors critical physiological parameters, including SpO2, heart rate, and body temperature, enabling proactive health interventions. A DT definition language (Digital Twin Definition Language)‐based time series analysis and twin graph platform further enhance sensor connectivity and scalability. Additionally, the integration of machine learning (ML) strengthens predictive accuracy, achieving 98% real‐time accuracy and 99.58% under cross‐validation (cv = 20) using the XGBoost algorithm. Empirical results demonstrate substantial improvements in processing time, system stability, and learning capacity, with real‐time predictions completed in 17 ms. This framework represents a significant advancement in healthcare monitoring, offering a responsive and scalable solution to latency and resource constraints in real‐time applications. Future research could explore incorporating additional sensors and advanced ML models to further expand its impact in healthcare applications.

Unraveling Ruminant Feed Efficiency Through Metabolomics: A Systematic Review

Background: Advancements in metabolomic technologies have revolutionized our understanding of feed efficiency (FE) in livestock, offering new pathways to enhance both profitability and sustainability in ruminant production. Methods: This review offers a critical and systematic evaluation of the metabolomics methods used to measure and assess FE in ruminants. We conducted a comprehensive search of PubMed, Web of Science, and Scopus databases, covering publications from 1971 to 2023. This review synthesizes findings from 71 studies that applied metabolomic approaches to uncover the biological mechanisms driving interindividual variations in FE across cattle, sheep, goats, and buffaloes. Results: Most studies focused on cattle and employed targeted metabolomics to identify key biomarkers, including amino acids, fatty acids, and other metabolites linked to critical pathways such as energy metabolism, nitrogen utilization, and muscle development. Despite promising insights, challenges remain, including small sample sizes, methodological inconsistencies, and a lack of validation studies, particularly for non-cattle species. Conclusions: By leveraging state-of-the-art metabolomic methods, this review highlights the potential of metabolomics to provide cost-effective, non-invasive molecular markers for FE evaluation, paving the way for more efficient and sustainable livestock management. Future research should prioritize larger, species-specific studies with standardized methods to validate identified biomarkers and enhance practical applications in livestock production systems.

Cognitive digital twin in manufacturing process: integrating the knowledge graph for enhanced human-centric Industry 5.0

Industry 5.0 emphasises human-centric intelligent manufacturing, posing challenges in integrating human expertise with advanced machine capabilities. To address these challenges, a novel three-layer cognitive digital twin model based on knowledge graphs is proposed, designed to integrate workers’ knowledge and experience into intelligent manufacturing processes. This model comprises three layers: an ontology layer that constructs a foundational process knowledge ontology library; a knowledge layer that maps real-time data to dynamically update digital models; and a cognitive layer that utilises machine learning, knowledge reasoning, and knowledge mining for advanced analysis, state understanding, and model evolution. The model promotes user interaction through intuitive interfaces and a Q&A system, leveraging techniques such as knowledge reasoning and querying to support decision-making and enhance worker engagement. Validated through a system implemented for aero-engine blade production, this cognitive digital twin model leverages human expertise and machine capabilities to enhance process control, quality management, and overall efficiency. The proposed approach demonstrates significant potential for advancing personalised human-machine interaction in manufacturing, truly embodying the value of a human-centric approach and paving the way for future developments in the field.

The Effectiveness of Electronic Medical Records (RME) On Hospital Service Quality

This study investigates The Medical Record Department (MRD) plays a pivotal role in the hospital administration framework at Shri Balaji Hospital, ensuring the efficient management and organization of patient health data. As an essential component of healthcare delivery, the MRD is responsible for maintaining accurate, comprehensive, and up-to-date records for all patients treated at the facility. This includes not only clinical data such as diagnosis, treatment plans, and surgical histories, but also administrative information vital for billing, insurance, and regulatory compliance. At Shri Balaji Hospital, the MRD operates under strict protocols to guarantee the confidentiality and security of patient records in accordance with legal and ethical standards. The department is crucial for the coordination of multidisciplinary care, as it facilitates the timely and accurate sharing of medical information between healthcare professionals, thus ensuring continuity of care and improving patient outcomes. The MRD also plays a significant role in hospital operations by supporting functions such as audits, quality control, and risk management. It ensures that the hospital adheres to national healthcare standards, including those set by accrediting bodies and government regulations. In this regard, the department’s meticulous record-keeping supports the hospital’s efforts in continuous quality improvement and patient safety initiatives.

What explains profit inefficiency in the restaurant industry? Evidence from Sweden

This study investigates the drivers of profit inefficiency in the restaurant industry. Assuming that restaurants operate under monopolistic competition, we first present a characterization of the factors that explain restaurants’ departure from the profit frontier, focusing on the role of business experience, number of outlets, service model, restaurant typology, and COVID-19 shock. Based on Stochastic Frontier Analysis, we estimate how these factors explain the differences in profit inefficiency across firms using a panel dataset of 4128 restaurants in Sweden in the period 2017–2021. We find that the average level of profit efficiency is low (46%). Inefficiency increases with years in operation and is significantly higher among limited-service restaurants and single-outlet firms. Interestingly, no significant difference in profit inefficiency is detected during the COVID-19 pandemic. Our findings provide valuable insights for profit efficiency management.

Enhancing municipal solid waste management efficiency through clustering: A case study

ABSTRACT This study leverages real-time datasets generated through IoT technology and smart city applications to enhance solid waste management in Yalova Province, Turkey. By integrating these datasets with the municipality’s Geographic Information System (GIS) using the ITRF/96 3 UTM X Y Coordinate System, a dynamic waste collection framework was established. The K-Means clustering algorithm was employed to determine the optimal waste container placement, considering capacities of 550, 800, 1,000, and 3,000 liters and walking distances of 50–100 ms. Results indicated that 1,000 and 3,000-liter containers with a 100-m walking distance maximized collection efficiency. Replacing 484 traditional containers with 105 units of 3,000 liters reduced total routes by 34%, transport costs by 42.2%, and CO2 emissions by 33.5%. The study underscores the importance of integrating GIS and IoT technologies for real-time waste management, aligning with the UN’s Sustainable Development Goals (SDG 11 and SDG 13). By combining data-driven decision-making with urban sustainability practices, it offers a replicable model for municipalities seeking to reduce costs and environmental impacts in waste collection.

SRFACS: A secure and robust framework for anonymous communication systems.

Anonymous communication is crucial for preserving user privacy in various applications, such as anonymous browsing, secure online payments, and electronic voting. However, current systems face significant challenges related to robustness, fault tolerance, and efficient communication management. This paper introduces SRFACS (Secure and Robust Framework for Anonymous Communication Systems), designed to address these issues by integrating advanced cryptographic techniques with a structured communication framework. Traditional anonymous communication systems usually lack sufficient fault tolerance, making them vulnerable to node failures, especially in asynchronous environments. In order to overcome these limitations and ensure that the system can keep running in the event of node failure (especially in an asynchronous environment), SRFACS integrates an improved asynchronous Byzantine Fault Tolerant (ABFT) protocol. We use this protocol to expand the traditional single communication node into a structured node group, and handle up to (n - 1)/3 faulty nodes in these groups, so as to enhance fault tolerance and maintain continuous operation. To efficiently manage inter-group communication, SRFACS utilizes leader nodes to coordinate and streamline the communication processes. To mitigate the risks associated with leader failures, we have implemented an efficient leader change protocol that promptly replaces defective leaders, ensuring uninterrupted system performance. Additionally, to prevent erroneous leader actions from compromising the system, we have introduced an advanced multi-signature scheme. This approach secures communication by requiring multiple signatures for verification processes. Furthermore, we have implemented a reputation incentive mechanism to encourage nodes to maintain optimal performance and avoid malicious behavior. This mechanism evaluates nodes according to their past activities and reliability to achieve dynamic updates of SRFACS node groups. We have conducted rigorous security analyses and component performance evaluations of SRFACS, further confirming its potential as a promising secure communication solution.

Strategic Insights into Bank Performance: CAMELS Analysis of Indian Banks

The banking sector is a cornerstone of the global economy, driving economic growth, financial sta-bility, and efficient resource allocation. Assessing the financial health and operational effectiveness of banks is crucial for maintaining the stability of the broader financial system. The CAMEL model, a well-established analytical framework, provides a comprehensive approach to evaluating key aspects of bank-ing performance, including Capital Adequacy, Asset Quality, Management Efficiency, Earnings Quality, and Liquidity. This model highlights areas for improvement and ensures a thorough assessment of a bank’s financial soundness. Extensive research has confirmed the model's applicability across various global contexts, including India, Nepal, Pakistan, Saudi Arabia, and Sri Lanka, consistently identifying asset quality, capital adequacy, and earnings as essential drivers of profitability. The model also reveals that private banks generally outperform public banks in most parameters. Moreover, the CAMEL model has proven to be a valuable tool in promoting financial inclusion, particularly through the analysis of Small Finance Banks, and its adaptability to emerging markets further underscores its relevance. Despite its broad applicability, gaps exist in the literature, particularly regarding the model’s adaptation to diverse regulatory environments and market dynamics. Addressing these gaps will enable more customized strategies for financial analysis and risk management, enhancing the resilience of the global banking system. This study aims to contribute to this discourse by exploring the nuanced use of the CAMEL model in assessing the financial performance of banks in varied contexts. The findings aim to offer ac-tionable insights for improving financial performance, managing risks, and fostering sustainable growth in the banking industry.

Research on SOC Prediction of Lithium-Ion Batteries Based on OLHS-DBO-BP Neural Network

Accurately estimating the state of charge (SOC) of lithium-ion batteries is of great significance for extending battery lifespan and enhancing the efficiency of energy management. Regarding the issue of the relatively low estimation accuracy of SOC by the backpropagation neural network (BPNN), an enhanced dung beetle optimizer (DBO) algorithm is proposed to optimize the initial weights and thresholds of the BPNN. This overcomes the drawback of a single BP neural network being prone to local optimum and accelerates the convergence rate. Simulation analyses on the experimental data of NCM and A123 lithium batteries were conducted in Matlab R2022a. The results indicate that the proposed algorithm in this paper has an average SOC estimation error of less than 1.6% and a maximum error within 2.9%, demonstrating relatively high estimation accuracy and robustness, and it holds certain theoretical research significance.

Practical Application and Management of Information Communication Technology (ICT) to Enhance the Performance of Ugandan Secondary Schools in West Nile

This study focuses on the contribution that Information and Communication Technology (ICT) can make to improving teacher and student performance in secondary schools in West Nile Region, Uganda. Examines the existing ICT implementation, assesses integration and potential benefits issues, and analyses the relationship between ICT usage and learning outcomes. The research is focused on showing that ICT can go global and how it connects Uganda’s secondary schools in West Nile to global education networks. ICT enhances global learning through active participation, innovation, and flexibility through access to international resources and embracing cross-boundary collaboration and virtual interchanges. This has improved student achievement but also endows students and teachers with the competencies to prosper in a globally connected environment. Ugandan schools have poorly developed ICT facilities, but schools embrace ICT education and facilities for operations. The study centers on specific ICT issues in West Nile schools and explores the possibility of using ICT to raise aggregate performance and efficiency in communication, collaboration, and organizational management. Quantitative and qualitative data were collected from 400 respondents, including teachers and students from 10 secondary schools. Data collection tools included questionnaires and interviews. Quantitative analysis was performed using SPSS, while NVivo was used for qualitative analysis. Ethical considerations were strictly adhered to protect participants' rights. While observing ICT integration in teaching across the ten selected secondary schools in West Nile and surveying 100 teachers, researchers found that 55.6% of them sometimes, 33.3% consistently, and 11% seldom integrate ICT in their teaching. This limited integration is due to a lack of ICT equipment, for example, computers, projectors, and internet connections; inadequate teacher education, where the majority of the teachers are found to be either lacking skills or self-confidence to incorporate ICT in teaching; and limited resource availability where even schools that have procured ICT tools are most often found to be having very few that are inadequacy for the needs of both teacher and students to make effective use of. These results raise concerns regarding the existing disparities in developments and funds for ICT training in West Nile’s secondary schools, with recommendations being made to enhance specific plans to reduce the digital divide. These include making ICT tools a focal area, increasing internet connection, and providing training activities that would increase and develop the competencies of the teaching staff. They seek to devise a technological learning atmosphere to enhance education, teacher, and student learning outcomes

Investigation and application of big data and AI-Powered provincial power grid accurate planning support platform

ABSTRACT This study presents an accurate planning assistance platform for big data and artificial intelligence-based provincial power grids. The platform greatly increases the efficiency of data integration and management by building a planning big database containing fundamental geographic information, thematic data, remote sensing image data, and planning business data. Technically speaking, this paper addresses the issues of accuracy and speed in the processing of remote sensing image data by using deep learning algorithms like convolutional neural networks (CNN) and full convolutional neural networks (FCN) to achieve automatic classification of high-resolution optical remote sensing images and residential area extraction. In the meantime, the intelligent routing approach, which is based on cost surface modelling and multi-criteria decision-making, optimises the routing of power lines by carefully taking into account a wide range of variables, including land use, accessibility for vehicles, and environmental impact. In addition to resolving the primary issues with present power grid planning, this study’s exact planning support platform for provincial power grids greatly enhances real-time, accurate, and scientific planning.

Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems

The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework’s applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes.

Intelligent fuzzy-PSO based grid connected solar EV charging station for electric buses to enhance stability and energy management

Abstract Modern power networks are relying more and more on Distributed Generation (DG) and solar energy-powered Electric Bus (EB) charging stations; nevertheless, controlling the charging process under situations of peak EB load and variable solar irradiation presents considerable hurdles. This study presents a coordinated control approach that maximizes energy distribution for solar (PV)-based EB charging stations by using an intelligent energy management system (IEMS). Through the analysis of load demand and meteorological data in real time, the IEMS optimizes PV generation and grid power consumption, thereby halving peak power demand, minimizing system losses, and easing distribution grid stress. In order to enable dynamic modifications to the energy flow between PV power, the grid, and battery storage, the method integrates an adaptive neuro-based fuzzy control system that anticipates solar energy generation and predicts EB load demands. Furthermore, to balance power distribution between battery and ultracapacitor systems and control energy storage in ultracapacitors, a fuzzy inference system optimized using Particle Swarm Optimization (PSO) is utilized. This prolongs battery life and guarantees effective energy management. The overall performance of the IEMS is improved by the fuzzy logic controller, which produces output signals that specify the best power distribution for any energy storage system. Digital simulations and a real-time hardware-in-loop experimental setup are used to validate the effectiveness of the proposed system, which shows notable gains in energy management, grid stability, and system efficiency. In order to improve intelligent energy management in grid-connected solar-powered systems, this study offers a viable method for incorporating renewable energy sources into EB charging stations.